Dr. Pratik Shrestha Profile

Dr. Pratik Shrestha

at Technische Univ. Delft

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Proceedings Article | 1 April 2019 Paper

Image analysis for classification of damaged and undamaged areas on composite structures

Pratik Shrestha, Roger Groves

Proceedings Volume 10971, 109711C (2019) https://doi.org/10.1117/12.2513430

KEYWORDS: Image analysis, Inspection, Composites, Thermography, Nondestructive evaluation, Algorithm development, Damage detection, Infrared imaging, Feature extraction

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Composite materials are susceptible to barely visible impact damages (BVID) due to low-velocity impact. Therefore, an automated damage detection and quantification technique is highly desirable for quick inspection of large number of composite structures. Among various different non-destructive techniques (NDTs), active thermography NDT can be used for detecting damage on aircraft structures, using an infrared camera to capture the temperature distribution on the structure after it is exposed to heat using a flash lamp. In this paper, an image analysis algorithm that analyzes the infrared image, acquired using NDTherm NT, by determining the changes in the colormap values to automate the detection and quantification of the damage size was proposed. An area of the second derivative pre-processed grayscale image acquired using NDTherm NT is scanned in the x-direction and y-direction, and for each scan region the histogram of colormap values is extracted and stored. Irregularities in the structure result in non-uniform temperature distributions, which cause the infrared image to have a wide-range of grayscale colormap values in the damaged area. Therefore, the damage region is identified by monitoring the changes in the number of detected grayscale colormap values. The proposed image analysis technique was implemented for automated damage detection on Boeing 787 skin’s curved CFRP panel, with dimensions of 1.3 × 1.3 m. The proposed method detected the damage and determined the maximum damage length in the x-direction and y-direction to be 70.1 mm and 57.8 mm, respectively. Moreover, the proposed technique is suitable for feature identification applications.

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